A new area of technology with increasing importance is the domain “collaborative filtering” or “social filtering” of information. These technologies represent a novel approach to information filtering that does not rely on the “content” of objects as is the case for content-based filtering. Instead, collaborative filtering relies on meta-data “about” objects. This meta data can either be collected automatically, i.e., data is inferred from the users' interactions with the system (for instance by the time spent reading articles as an indicator of interest), or data is voluntarily provided by the users of the system.
In essence, the main idea is to automate the process of “word-of-mouth” by which people recommend products or services to one another. If one needs to choose between a variety of options with which one does not have any experience, one will often rely on the opinions of others who do have such experience. However, when there are thousands or millions of options, like in the Web, it becomes practically impossible for an individual to locate reliable experts who can give advice about each of the options.
By shifting from an individual to a collective method of recommendation, the problem becomes more manageable. Instead of asking the opinions of individuals, one might try to determine an “average opinion” for the group. This, however, ignores an individual's particular interests, which may be different from those of the “average person”. Therefore one would rather like to hear the opinions of those people who have interests similar to one's own that is to say, one would prefer a “division-of-labor” type of organization, where people only contribute to the domain they are specialized in.
The basic mechanism behind collaborative filtering is the following:                a large group of peoples' preferences are registered;        using a similarity metric, a subgroup of people is selected whose preferences are similar to the preferences of the person who seeks advice;        a (possibly weighted) average of the preferences for that subgroup is calculated;        the resulting preference function is used to recommend options on which the advice-seeker or advisee has expressed no personal opinion as yet.        
Typical similarity metrics are Pearson correlation coefficients between the users' preference functions and (less frequently) vector distances or dot products. If the similarity metric has indeed selected people with similar tastes, the chances are great that the options that are highly evaluated by that group will also be appreciated by the advicee.
A typical application of collaborative filtering is the recommendation of books, music CDs, or movies. More generally, the method can be used for the selection of documents, services, products of any kind, or in general any type of resource. In the world before the Internet, rating and recommendations were provided by services such as:                Newspapers, magazines, and books, which are rated by their editors or publishers, selecting information which they think their readers might want.        Consumer organizations and trade magazines which evaluate and rate products.        Published reviews of books, music, theater, films, etc.        Peer review method of selecting submissions to scientific journals.        
Examples of these technologies are, for instance, the teachings of John B. Hey, “System and method of predicting subjective reactions”, U.S. Pat. No. 4,870,579 or John B. Hey, “System and method for recommending items”, U.S. Pat. No. 4,996,642, both assigned to Neonics Inc., as well as Christopher P. Bergh, Max E. Metral, David Henry Ritter, Jonathan Ari Sheena, James J. Sullivan, “Distributed system for facilitating exchange of user information and opinion using automated collaborative filtering”, U.S. Pat. No. 6,112,186, assigned to Microsoft Corporation.
In spite of all these advances and especially due to the increased importance of the Internet, which provides the access technology and communication infrastructure to recommendation systems, there is still a need in the art for improvement. Consequently, the invention has an objective of improving performance and efficiency when handling a flood of recommendation requests. It is a further objective of the current invention to improve the quality of the individual recommendations.